ILOG CPLEX 11.0 User's Manual

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RHSrhs R118 -5rhs R119 2700rhs R120 -2600rhs R121 -100rhs R122 -900rhs R123 -1656rhs R124 -335rhs R125 -1026rhs R126 -5rhs R127 -500rhs R128 -270rhs QC1 1rhs QC2 2rhs QC3 1rhs QC4 1BOUNDSUP bnd C157 1UP bnd C158 1UP bnd C159 1UP bnd C160 1UP bnd C161 1UP bnd C162 1UP bnd C163 1UP bnd C164 1UP bnd C165 1UP bnd C166 1UP bnd C167 1UP bnd C168 1UP bnd C169 1UP bnd C170 1UP bnd C171 1UP bnd C172 1UP bnd C173 1UP bnd C174 1UP bnd C175 1UP bnd C176 1UP bnd C177 1UP bnd C178 1UP bnd C179 1UP bnd C180 1UP bnd C181 1UP bnd C182 1UP bnd C183 1UP bnd C184 1UP bnd C185 1UP bnd C186 1UP bnd C187 1UP bnd C188 1UP bnd C189 1248 ILOG CPLEX 11.0 — USER’ S MANUAL
QMATRIXC158 C158 1C158 C189 0.5C189 C158 0.5C189 C189 1QCMATRIX QC1C157 C157 1C157 C158 0.5C158 C157 0.5C158 C158 1C159 C159 1C160 C160 1QCMATRIX QC2C161 C161 2C162 C162 2C163 C163 1QCMATRIX QC3C164 C164 1C165 C165 1QCMATRIX QC4C166 C166 1C167 C167 1C168 C168 1C169 C169 1C171 C171 1ENDATAChanging Problem TypeBy default, every model in Concert Technology is the most general problem type possible.Consequently, it is not necessary to declare the problem type nor to change the problem type,even if you add quadratic constraints to the model or remove them from it. In contrast, boththe Callable Library and the Interactive Optimizer need for you to indicate a change inproblem type explicitly if you remove the quadratic constraints that make your model a QCP.In both the Callable Library and Interactive Optimizer, if you want to remove the quadraticconstraints in order to solve the problem as an LP or a QP, then you must first change theproblem type, just as you would, for example, if you removed the quadratic coefficients froma quadratic objective function.From the Callable Library, use the routine CPXchgprobtype to change the problem type toCPXPROB_LP if you remove the quadratic constraints from your model in order to solve it asan LP. Contrariwise, if you want to add quadratic constraints to an LP or a QP model andthen solve it as a QCP, use the routine CPXchgprobtype to change the problem type toCPXPROB_QCP.When using the Interactive Optimizer, you apply the command change problem with oneof the following options:ILOG CPLEX 11.0 — USER’ S MANUAL 249
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ILOG CPLEX 11.0User’s ManualSepte
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C O N T E N T STable of ContentsILO
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Building the Model by Column . . .
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Program Description . . . . . . . .
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Cholesky Factor in the Log File . .
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Examples: QCP . . . . . . . . . . .
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Chapter 18 Using Piecewise Linear F
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Representing the Data. . . . . . .
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Reading and Writing MPS Files . . .
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Protected Variables in Presolve Red
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P R E F A C EMeet ILOG CPLEXILOG CP
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When a linear optimization problem
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◆◆◆◆implemented in ILOG CPL
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Solution Pool: Generating and Keepi
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Table 1ExamplesExample Source File
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◆◆◆◆◆◆Overview of the A
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Wolsey, Laurence A., Integer Progra
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C H A P T E R1ILOG Concert Technolo
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Compiling and LinkingCompilation an
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The class IloNumVar provides method
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IloModel object itself and added to
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Table 1.1 Concert Technology Modeli
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Choosing an OptimizerSolving the ex
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Table 1.4 on page 55 summarizes the
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Dynamic control of the solution pro
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However, querying solution values v
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parameter for the getQuality method
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model2.add(model1);model2.add(IloCo
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Example: Optimizing the Diet Proble
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Concert Technology, as demonstrated
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method IloModel::add returns the mo
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Licenses in a Java ApplicationILOG
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Models that consist only of such co
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The special case of linear expressi
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IloObjective obj = cplex.add(cplex.
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Solving the ModelOnce you have crea
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IloCplex cplex = new IloCplex();Ilo
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IloCplex.setParam(IloCplex.IntParam
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Thus, the suggested method for sett
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Basis InformationWhen solving an LP
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where A is a sparse matrix. A spars
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The example starts by evaluating th
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IloMPModeler.setLinearCoefs, and Il
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◆What is the purpose (the objecti
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Step 3Create the modelGo to the com
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Step 9Add nutritional constraintsGo
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Step 14Display the solutionGo to th
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Step 18Enclose the application in t
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C H A P T E R4ILOG CPLEX Callable L
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◆◆file reading and writing rout
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◆◆If data already exist in MPS,
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For example, let’s look at the sy
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As you modify a problem object thro
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However, ILOG CPLEX updates the nam
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whether an optimization should be a
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Callable Library have names greater
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◆CPXsetstrparam accepts arguments
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Here’s another way to visualize a
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program—can discover the length o
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Part IIProgramming ConsiderationsTh
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◆ Test Data on page 135◆ Choose
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◆ Use the MIP optimizer if the pr
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Choose Clarity First, Efficiency La
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1. If you can reproduce the behavio
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indices, errors will occur. Therefo
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Understanding File FormatsThe refer
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definitions that it finds. The firs
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◆◆IloXmlReader creates a reader
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Controlling Message ChannelsIn both
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more calls to the message handling
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Concert Technology Message Channels
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Types of ILM Runtime LicensesILM ru
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char *inststr = NULL;char *envstr =
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The registerLicense Method for Java
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not correct that problem either. In
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In the Interactive Optimizer, you c
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For models not in the current worki
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◆◆In the .NET API, pass an inst
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C H A P T E R9Solving LPs: Simplex
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The symbolic names for these settin
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When no candidates are present, the
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solution; but examination of soluti
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Then to later read an advanced basi
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Table 9.5 PPriInd Parameter Setting
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ILOG CPLEX ignores the coefficients
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Numeric DifficultiesILOG CPLEX is d
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esult is the same as would be obtai
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automatically perturbs the variable
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4. Consider alternate scalings.You
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smaller in absolute value than the
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exceptions are caught as well, and
Page 197 and 198: C H A P T E R10Solving LPs: Barrier
Page 199 and 200: The ILOG CPLEX Barrier Optimizer is
Page 201 and 202: And then you call the solution rout
Page 203 and 204: Here is an example of a log file fo
Page 205 and 206: If solution values are large in abs
Page 207 and 208: Normalized errors, for example, rep
Page 209 and 210: ◆ workmem in the Interactive Opti
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Page 213 and 214: choice of starting-point heuristic
Page 215 and 216: To see the current value of the col
Page 217 and 218: C H A P T E R11Solving Network-Flow
Page 219 and 220: ◆◆l a , the lower bound, sets t
Page 221 and 222: of the objective function calculate
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Page 225 and 226: -a 1 + a 2 - a 8 - a 9 + a 14 = 0-
Page 227 and 228: C H A P T E R12Solving Problems wit
Page 229 and 230: convex QPs, Q must be positive semi
Page 231 and 232: A similar Java program using Concer
Page 233 and 234: ◆ qp indicates that you want ILOG
Page 235 and 236: RootAlg parameter (QPMETHOD in the
Page 237 and 238: ilolpex1.cpp counterpart. Here the
Page 239 and 240: C H A P T E R13Solving Problems wit
Page 241 and 242: Figure 13.2y(0, 1)(-1, 0)d(1, 0)xc(
Page 243 and 244: Detecting Problem TypeILOG CPLEX de
Page 245 and 246: COLUMNSMARK0000 'MARKER''INTORG'C15
Page 247: C180 R100 159C180 R119 200C180 R120
Page 251 and 252: ◆From the Callable Library, use t
Page 253: Part IVDiscrete OptimizationThis pa
Page 256 and 257: Stating a MIP ProblemA mixed intege
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Page 260 and 261: ◆milp, miqp, or miqcpindicating t
Page 262 and 263: 1. finding a succession of improvin
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Page 266 and 267: If the solution to the relaxation s
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Page 270 and 271: directives about the order in which
Page 272 and 273: ◆ Parameters Affecting Cuts on pa
Page 274 and 275: ◆●●IloCplex.getNcuts in the J
Page 276 and 277: Relaxation Induced Neighborhood Sea
Page 278 and 279: Table 14.10Parameters for Controlli
Page 280 and 281: Library). If this process succeeds,
Page 282 and 283: After you have solved a MIP, you wi
Page 284 and 285: Table 14.13Settings of the MIP Disp
Page 286 and 287: ILOG CPLEX also logs its addition o
Page 288 and 289: ◆ Parent specifies the NodeID of
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Page 292 and 293: When you set a MIP cutoff value, IL
Page 294 and 295: memory. It also includes several op
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◆In the Callable Library, use the
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●●Then call CPXprimopt to optim
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◆ The Incumbent and the Solution
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c17: x1 - y11 >= 0c18: x1 - y21 >=
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Populating the Solution PoolILOG CP
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NodesCuts/Node Left Objective IInf
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Note: The parameter to limit the nu
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Example: Using Populate after MIP O
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Limitations Due to Numeric Difficul
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Examining the Solution PoolIn the I
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For a sample of these methods or ro
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◆◆In the Callable Library (C AP
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Then display the objective value of
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◆ If you want to filter solutions
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●●●In the C++ API, use the me
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NAME locationRNGFILTER f2 -inf 0tra
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continuous, a model containing one
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◆The routine setPriorities sets t
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What Are Semi-Continuous Variables?
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With that model, now the applicatio
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Piecewise Linearity in ILOG CPLEXSo
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Reminder: It may help you understan
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overlaps with an endpoint of two ot
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Figure 18.4400003000020000100000Fig
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in demand; in terms of this model,
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348 ILOG CPLEX 11.0 — USER’ S M
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What Are Logical Constraints?For IL
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◆◆Cplex.NotCplex.IfThenAgain, t
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In Concert Technology applications,
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Describing the ProblemThe problem i
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Notice that how much to use and buy
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Then use a for-loop to add the cons
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These lines (the action of the if-s
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Describing the ProblemThe problem i
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Stating Precedence ConstraintsIn ea
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Solving the ProblemAn emphasis on f
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What Is Column Generation?In colloq
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Solving this model with all columns
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Likewise, the application adds an a
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Pattern Generator ModelThe submodel
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Complete ProgramYou can see the ent
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your problem formulation caused thi
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To control the types of reductions
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What Is Unboundedness?Any class of
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Callable Library use the advanced r
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the conflict to arrive at a minimal
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the IIS finder can, and often more.
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Then you will see results like thes
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Now view the entire conflict with t
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The constraints in conflict with th
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If a model contains more than one c
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Immediately after that statement, i
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◆ Groups in the Conflict Refiner
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The infeasibility on which FeasOpt
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Now the following lines invoke Feas
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suggests decreasing the upper bound
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C H A P T E R28User-Cut and Lazy-Co
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However, there is an important dist
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◆◆Through the routine CPXreadco
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Here is an example of an MPS file e
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C H A P T E R29Using GoalsThis chap
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How Goals Are Implemented in Branch
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In the Java API, a Fail goal is ret
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This return statement returns an An
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The Goal StackTo understand how goa
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soon as they are enclosed in a goal
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The global cut goal for lhs[i] ≤
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If a node has multiple evaluators a
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As this example is an extension of
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C H A P T E R30Using Optimization C
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Reference Documents about Informati
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Query or Diagnostic CallbacksQuery
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● Cplex.DisjunctiveCutCallback in
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◆◆●●Cplex.HeuristicCallback
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It is not customary to write such a
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IloCplex::Callback mycallback = cpl
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Here is the previous sample of code
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◆◆cbdata, a pointer to ILOG CPL
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conventions of the Interactive Opti
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een added, it can be deleted by cal
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Interaction Between Callbacks and I
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C H A P T E R31Goals and Callbacks:
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The only functionality that is not
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C H A P T E R32Advanced Presolve Ro
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once. All of the node relaxation so
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- 5x1, + x2 ≤ 0 2y1, + y2, ≥ 1x
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problem, the problem has a presolve
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Modifying a ProblemThis section bri
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C H A P T E R33Advanced MIP Control
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value strictly greater than one. It
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Cut CallbackThe next example we con
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problem. Since branching involves a
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C H A P T E R34Parallel OptimizersT
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Threads and Performance Considerati
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3. Call the parallel optimizer with
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optimizer turns out to be the faste
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0 0 3383.7784 16 4505.0000 Cuts: 35
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I N D E XIndexAabsolute objective d
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ole in converting LP to network flo
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emoving from basis (C++ API) 62repr
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CPX_PARAM_PCPX_PARAM_POLISHTIMEsolu
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CPXcreateprob 467CPXcreateprob rout
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CPXwcpxwarning message channel 151C
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empty goal 431, 435end methodIloEnv
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getDuals method (Java API) 88getMax
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emove method (Java API) 94IloModele
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ecords singularities 188relocating
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from 218head 218sink 219source 219s
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control callbacks and 453query call
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primal reduction 385primal simplex
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eturn statusBounded (Java API) 81Er
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solvingdiet problem (Java API) 82mo
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arrier 208WorkMem 293WorkMem parame
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